EyeTAG：眼动轨迹感知的视线估计
EyeTAG: Eye Trajectory-Aware Gaze Estimation
- Soongsil University（崇实大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
EyeTAG提出因果多帧视线估计框架，利用显式一阶视线先验（差分轨迹标记）捕捉受试者不变运动，通过交叉注意力融合面部与眼睛流，在Gaze360上平均角度误差降低约1.0°，在EVE上与最强基线相当，消融实验证明差分公式消除系统性扫视偏差。
中文摘要 AI 辅助
自然头眼运动下的视线估计支撑着从驾驶员监控到人机交互等应用。单帧方法独立预测每一帧，因此连续输出会像抖动一样波动。多帧方法减少了这种波动，但它们在表观特征内部隐式地学习运动，因此视线轨迹从未成为显式变量。我们提出EyeTAG（眼动轨迹感知的视线估计），一种围绕显式一阶视线先验构建的因果多帧框架：在每一步，它区分自身最近的预测，并将由此产生的轨迹作为紧凑的运动学标记反馈回来。由于差分在视线空间中具有平移不变性，该标记携带受试者不变的运动而非个人视线偏移。面部和眼睛流提供视觉证据，通过交叉注意力和因果Transformer解码器融合。EyeTAG在Gaze360上将平均角度误差减少了约1.0°，并在EVE上与最强基线表现相当（2.56°对比2.58°）。在模型内消融实验中，保持编码器和架构其余部分固定，仅改变视线历史，结果表明差分公式而非仅时间上下文，消除了即使使用绝对视线历史先验也持续存在的系统性扫视偏差。我们的代码可在https此URL获取。
英文摘要
Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict each frame independently, so consecutive outputs fluctuate as jitter. Multi-frame methods reduce this, but they learn motion implicitly inside appearance features, so the gaze trajectory is never an explicit variable. We propose EyeTAG (Eye Trajectory-Aware Gaze Estimation), a causal multi-frame framework built around an explicit first-order gaze prior: at each step it differentiates its own recent predictions and feeds the resulting trajectory back as a compact kinematic token. Because differencing is translation-invariant in gaze space, this token carries subject-invariant motion rather than personal gaze offsets. Face and eye streams supply visual evidence, fused by cross-attention and a causal Transformer decoder. EyeTAG reduces the mean angular error by about 1.0$^\circ$ on Gaze360 and performs on par with the strongest baseline on EVE (2.56$^\circ$ vs. 2.58$^\circ$). Within-model ablations, which keep the encoder and the rest of the architecture fixed and vary only the gaze history, show that the differential formulation, rather than temporal context alone, removes the systematic saccade bias that persists even with an absolute gaze-history prior. Our code is available at https://github.com/peter8366/EyeTAG.
